SoroushVahidi/scidocs huggingface dataset:
Object Detection with Active Sample Harvesting
(2017)

The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between populations of samples. In practice, all the samples are not equally important: some examples are trivially classified and do not bring much to the training, while others close to the boundary or misclassified are the ones that truly matter. Similarly, images where the samples originate from are not all rich in informative samples. However, most training procedures select samples and images uniformly or weigh them equally. The common thread of this dissertation is how to efficiently find the informative samples/images for training. Although we never consider all the possible samples “in the world”, our purpose is to select the samples in a smarter manner, without looking at all the available ones. The framework adopted in this work consists in organising the data (samples or images) in a tree to reflect the statistical regularities of the training samples, by putting “similar” samples in the same branch. Each leaf carries a sample and a weight related to the “importance” of the corresponding sample, and each internal node carries statistics about the weights below. The tree is used to select the next sample/image for training, by applying a sampling policy, and the “importance” weights are updated accordingly, to bias the sampling towards informative samples/images in future iterations. Our experiments show that, in the various applications, properly focusing on informative images or informative samples improves the learning phase by either reaching better performances faster or by reducing the training loss faster.
Behavioral resource-aware model inference
(2014)

Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.
 Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.
High-level visual features for underwater place recognition
(2015)

This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.
Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects.
(2005)

The hypothesis was tested of whether neurofeedback training (NFT)--applied in order to increase upper alpha but decrease theta power--is capable of increasing cognitive performance. A mental rotation task was performed before and after upper alpha and theta NFT. Only those subjects who were able to increase their upper alpha power (responders) performed better on mental rotations after NFT. Training success (extent of NFT-induced increase in upper alpha power) was positively correlated with the improvement in cognitive performance. Furthermore, the EEG of NFT responders showed a significant increase in reference upper alpha power (i.e. in a time interval preceding mental rotation). This is in line with studies showing that increased upper alpha power in a prestimulus (reference) interval is related to good cognitive performance.
The Impact of Blogging and Scaffolding on Primary School Pupils' Narrative Writing: A Case Study
(2010)

Narrative writing is a skill that all primary (elementary) school pupils in Singapore are required to develop in their learning of the English language. However, this is an area in which not all pupils excel. This study investigates if the use of blogging and scaffolding can improve pupils’ narrative writing. Data were gathered from 36 primary five (grade five) pupils through pre-post writing tests, reflection sheets, and interviews. The pre-post writing tests were administered before and after the pupils had completed their blogging activities, while the blogs were used to draft their narrative writings and to comment on their peers’ writings. The teacher also used a writing guide that served as a scaffold to help pupils plan their writing on their blogs. Overall, results showed a statistically significant difference of medium effect size between the pre-post test scores. Pupils’ perceptions of using blogs as a tool for writing were also explored. attend school (Huffaker, 2004). In order to have success throughout life, verbal literacy is crucial and especially so from the beginnings of education to the future employment of adults (Cassell, 2004). In Singapore, the primary education system consists of a six-year program: a four-year foundation stage from primary (grade) 1 to 4 and a two-year orientation stage from primary 5 to 6 (Ministry of Education, 2010). One of the overall aims of the Singapore primary education is to give pupils a good grasp of the English Language. Literacy development such DOI: 10.4018/jwltt.2010040101 2 International Journal of Web-Based Learning and Teaching Technologies, 5(2), 1-17, April-June 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. as reading and writing is at the heart of the English Language instructional programme in Singapore schools (Curriculum Planning and Development Division (CPDD), 2001). As indicated in the 2001 English Language Syllabus
Combined software-defined network (SDN) and Internet of Things (IoT)
(2017)

The Internet of Things (IoT) is an impending technology that captured the industries and research interests in a brief period. Presently, the number of Internet-connected devices are estimated at billions and by 2020 many studies expect to have around 50 billion devices that are connected to the Internet. The IoT devices have produced a significant amount of data that could be threatening to the security and directing the flow of data in the network. One of the alternative solutions for problems caused by IoT is to have some kind of solutions that are based on programming and primary control. Software Define Network (SDN) offers a programmable and primary control for the basic network without variability of the current architecture of the network. This paper discusses some solutions for various domains for the combined SDN based on IoT and current directions in studies. The relative analysis of these solutions prepares a simple and brief view of those directions.
Approximation Algorithms
(2001)

There are many variations of local search: Hill-climbing: The name for the basic method described above. Metropolis: Pick a random y ∈ N(x). If f(y) < f(x), set x = y. If f(y) > f(x), set x = y with some probability. Simulated annealing: Like Metropolis, but where the probability of moving to a higher-cost neighbor decreases over time. Tabu search: Like the previous two, but with memory in order to avoid getting stuck in suboptimal regions or in plateaus (“taboo” areas). Parallel search: Do more than one search, and occasionally replace versions that are doing poorly with copies of versions that are doing well. Genetic algorithm: Keep a population of searches that changes over time via “breeding” of the best-performing searches.
Vitamins, minerals, and mood.
(2007)

In this article, the authors explore the breadth and depth of published research linking dietary vitamins and minerals (micronutrients) to mood. Since the 1920s, there have been many studies on individual vitamins (especially B vitamins and Vitamins C, D, and E), minerals (calcium, chromium, iron, magnesium, zinc, and selenium), and vitamin-like compounds (choline). Recent investigations with multi-ingredient formulas are especially promising. However, without a reasonable conceptual framework for understanding mechanisms by which micronutrients might influence mood, the published literature is too readily dismissed. Consequently, 4 explanatory models are presented, suggesting that mood symptoms may be expressions of inborn errors of metabolism, manifestations of deficient methylation reactions, alterations of gene expression by nutrient deficiency, and/or long-latency deficiency diseases. These models provide possible explanations for why micronutrient supplementation could ameliorate some mental symptoms.
NameClarifier: A Visual Analytics System for Author Name Disambiguation
(2017)

In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.
LegUp: high-level synthesis for FPGA-based processor/accelerator systems
(2011)

In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool.
Differential Evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces by
(1995)

A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
(2006)

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
Hybrid Particle Swarm Optimiser with breeding and subpopulations
(2001)

In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models. This is done to illustrate that PSOs with breeding strategies have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe how to make the hybrids benefit from genetic methods and to test their potential and competetiveness on function optimisation.
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
(2002)

The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. Even though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some undesirable dynamical properties, notably the particles’ velocities needed to be limited in order to control their trajectories. The present paper analyzes a particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.
Simulated annealing: Practice versus theory
(1993)

Simulated annealing (SA) presents an optimization technique with several striking positive and negative features. Perhaps its most salient feature, statistically promising to deliver an optimal solution, in current practice is often spurned to use instead modified faster algorithms, “simulated quenching” (SQ). Using the author’s Adaptive Simulated Annealing (ASA) code, some examples are given which demonstrate how SQ can be much faster than SA without sacrificing accuracy.
Deep Image Segmentation by Quality Inference
(2018)

Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations.
1 ACO Algorithms for the Traveling Salesman Problemy
(1999)

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect communication of a colony of agents, called (artificial) ants, based on pheromone trails (pheromones are also used by real ants for communication). The (artificial) pheromone trails are a kind of distributed numeric information which is modified by the ants to reflect their experience while solving a particular problem. Recently, the Ant Colony Optimization (ACO) metaheuristic has been proposed which provides a unifying framework for most applications of ant algorithms [15, 16] to combinatorial optimization problems. In particular, all the ant algorithms applied to the TSP fit perfectly into the ACO meta-heuristic and, therefore, we will call these algorithms also ACO algorithms. The first ACO algorithm, called Ant System (AS) [18, 14, 19], has been applied to the Traveling Salesman Problem (TSP). Starting from Ant System, several improvements of the basic algorithm have been proposed [21, 22, 17, 51, 53, 7]. Typically, these improved algorithms have been tested again on the TSP. All these improved versions of AS have in common a stronger exploita-
Variational shape approximation
(2004)

A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.
End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression
(2013)

When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons. * Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence © 2012 Elsevier B.V. All rights reserved.
On the Kinematic Analysis of Robotic Mechanisms
(1999)

The kinematic analyses, of manipulators and other robotic devices composed of mechanical links, usually depend on the solution of sets of nonlinear equations. There are a variety of both numerical and algebraic techniques available to solve such systems of equations and to give bounds on the number of solutions. These solution methods have also led to an understanding of how special choices of the various structural parameters of a mechanism influence the number of solutions inherent to the kinematic geometry of a given structure. In this paper, results from studying the kinematic geometry of such systems are reviewed, and the three most useful solution techniques are summarized. The solution techniques are polynomial continuation, Gröbner bases, and elimination. We then discuss the results that have been obtained with these techniques in the solution of two basic problems, namely, the inverse kinematics for serial-chain manipulators, and the direct kinematics of in-parallel platform devices. KEY WORDS—inverse kinematics, direct kinematics, manipulators, in-parallel manipulators, series manipulators, polynomial continuation, characteristic polynomial